Towards Trustworthy Biological Alignment in TabPFN-Probed Pathology Foundation Models

arXiv:2609.29523 · cs.MA, q-bio.QM · Submitted 2026-08-25 · Read on arXiv

cs.MA, q-bio.QM

Submitted: 2026-08-25

Updated: 2026-08-25

Terminology

• Foundation Model — 28× in this paper · explained in 3 episodes
• Alignment — 23× in this paper · explained in 1 episode
• Foundation Models — 15× in this paper · explained in 7 episodes
• Distribution Shift — 10× in this paper · explained in 5 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Embeddings — 5× in this paper · explained in 2 episodes
• Training-Free Framework — 4× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Genome-Anchored Foundation Model Embeddings Improve Molecular Prediction from Histology Images
• Towards Spatial Transcriptomics-driven Pathology Foundation Models
• Current Pathology Foundation Models are unrobust to Medical Center Differences
• Phikon-v2, A large and public feature extractor for biomarker prediction
• Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics
• Learning biologically relevant features in a pathology foundation model using sparse autoencoders
• MINT: Molecularly Informed Training with Spatial Transcriptomics Supervision for Pathology Foundation Models
• TIME: TabPFN-Integrated Multimodal Engine for Robust Tabular-Image Learning
• SPADE: Spatial Transcriptomics and Pathology Alignment Using a Mixture of Data Experts for an Expressive Latent Space
• Molecular-driven Foundation Model for Oncologic Pathology
• Images as Tables: In-Context Learning with TabPFN for Low-Data Detection of AI-Generated Images

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